Wavelet threshold methods used in lightning transient electrical signals denoising

Li, Peng; Zheng, Yi; Han, Chao; Song, Lei; Cao, Baofeng

doi:10.1109/fskd.2012.6234045

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…As in equation ( 9), M preserves the coefficients that meet certain conditions unchanged and sets the ones that are not satisfied to 0. For the wavelet denoising of the CSI, the penalty strategy model is used to calculate the threshold [26]:…”

Section: Secondary Filteringmentioning

confidence: 99%

Personnel Identification System Using the Wireless Channel Characteristics and Machine Learning

Li¹

2021

converter

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In the presence of a Wi-Fi network, movements of individuals lead to changes in the channel state information (CSI) of the Wi-Fi network. Thus, it is possible to use CSI to identify specific individuals based on walking gait and under movements. We have built a hardware system to collect CSI and developed CSI real-time acquisition and visualization software. We further improve results by preprocessing the CSI to remove noise by outlier removal, a low-pass filter, and a discrete wavelet transform. For identity recognition, we propose a parallel learning structure using a convolutional neural network (CNN) and bidirectional long-term and short-term memory network to extract amplitude and time sequence features of human gait at the same time. We then classify the two gait features using Softmax function to identify individuals. Our identity recognition experiment with 30 people yielded a maximum accuracy of 98.7% with our device and software. The system has a value of application in the field of smart home and intrusion detection.

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Section: Secondary Filteringmentioning

confidence: 99%

Personnel Identification System Using the Wireless Channel Characteristics and Machine Learning

Li¹

2021

converter

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“…Generally, noise associated with travelling waves are of varying degrees of frequencies, therefore further studies is proposed to investigate. Studies in this regard has been documented in [38][39][40] whilst external fault are shown in Figure 4.10. As, shown, there is a significant reduction in the peaks as well as a shift in the travelling wave components waveform.…”

Section: Fig 47 Noisy and Smoothed Current Waveform Measured At Thementioning

confidence: 99%

A novel time domain protection technique for multi-terminal HVDC networks utilising travelling wave energy

Ikhide

Tennakoon

Griffiths

et al. 2018

Sustainable Energy, Grids and Networks

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Fault vulnerability and protection issues are major challenge in realising multi-terminal HVDC transmission system, also termed HVDC grids. This paper presents a novel time domain and transient based protection technique for application to HVDC grids. The technique utilises the energy of the forward and backward travelling waves produced by a fault to distinguish between internal and external faults. For an internal fault, the calculated forward or backward travelling wave energy for a pre-set time duration following the occurrence of a fault must exceed a predetermined setting otherwise the fault is external. This characteristic is largely due to the DC inductor located at the cable ends, as per HVDC breakers or fault current limiters, which provides attenuation for the high frequency transients resulting from an external fault. The ratio between the forward travelling wave energy and the backward travelling wave energy provides directional comparison. For a forward directional fault with respect to a local relay, this ratio must be less than unity whereas the ratio is greater than unity for a reverse directional fault. The simulation results presented based on full scale Modular Multilevel Converter Based HVDC grid shows the suitability of the proposed technique. An advantage of this technique is that it is non-unit based and as such no communication delay is incurred. Furthermore, it is simple as it does not require complex mathematical/DSP technique; and as such can be easily implemented at each independent relay since it will require minimal hardware resources hence reduces cost.

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“…In such scenario, it is extremely useful to denoise lightning electric field signals. Some recent relevant contributions have proposed solutions using a wide range of different techniques, such as the wavelet transform [1][2][3][4][5], adaptive filtering [6][7][8].However, these methods have not been widely used to remove the undesired components from original lightning electric field signals, except for a few techniques based on the classic Fourier transform as well as on the wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

Denoising of Lightning Electric Field Signals Based on EMD-Wavelet Method

Huo¹,

Ping²,

Yongfeng³

2017

dtetr

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In this paper, a new lightning electric field (LEF) signals denoising approach combined with empirical mode decomposition (EMD) and wavelet transform (WT) is proposed. Unlike the conventional EMD or WT denoising approaches, we use EMD to decompose the LEF signal firstly, the continuous mean square error(CMSE) criteria are used to determine a turning point in the original signal energy, then the Birge-Massart threshold wavelet denoising method is employed to denoise the high frequency component which contains lots of noise. Finally, the clean high frequency component and the remaining low frequency intrinsic mode function, and the residual of the EMD operation are employed to synthesize a cleaner LEF signal. The method is illustrated on real data, and the performance of the proposed method is evaluated in terms of several standard metrics. The results show that the proposed method is able to reduce noise from the noisy LEF signals more accurately and effectively in comparison to EMD filtering and Wavelet filtering methods.

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Wavelet threshold methods used in lightning transient electrical signals denoising

Cited by 6 publications

References 2 publications

Personnel Identification System Using the Wireless Channel Characteristics and Machine Learning

Personnel Identification System Using the Wireless Channel Characteristics and Machine Learning

A novel time domain protection technique for multi-terminal HVDC networks utilising travelling wave energy

Denoising of Lightning Electric Field Signals Based on EMD-Wavelet Method

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